broadband trailing-edge noise · lest automatically converts rans statistics into ﬂuctuating...

Broadband Trailing-Edge Noise as a Canonical Benchmark Problem for Airframe Noise Predictions Michaela Herr Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Braunschweig, Germany 19th X-NOISE/CEAS Workshop: Broadband Noise of Rotors and Airframe 23–25 September 2015, La Rochelle, France

www.DLR.de • Chart 1 > M. Herr > 19th X-NOISE/CEAS Workshop > 23 September 2015, La Rochelle, France

Outcome of the BANC-III Workshop & Invitation for BANC-IV

EXTENDED UPLOAD VERSION

[email protected]

mailto:[email protected]

Workshops on Benchmark Problems for Airframe Noise Computations (BANC)

Introduction Motivation behind BANC activity

Objectives of the BANC workshops (since 2010) are - to provide a forum for a thorough assessment of simulation-based noise-

prediction tools; - to identify current gaps in physical understanding, experimental databases,

and prediction capability for the major sources of airframe noise; - to help determine best practices, and accelerate the development of

benchmark quality datasets; - to promote future coordinated studies.

www.DLR.de • Chart 2

https://info.aiaa.org/tac/ASG/FDTC/ DG/BECAN_files_/

https://info.aiaa.org/tac/ASG/FDTC/DGBECAN_files_/BANCII_category1

https://info.aiaa.org/tac/ASG/FDTC/DGBECAN_files_/BANCII_category1

Workshop categories: 1. Airfoil trailing edge noise (TEN) 2. Unsteady wake interference between a pair of inline tandem cylinders 3. Minimal 4-wheel landing gear 4. Partially-dressed, cavity-closed nose landing gear 5. The LAGOON Simplified Landing Gear configuration tested by

Airbus and ONERA 6. Slat Noise (DLR/ONERA Configuration) 7. Slat Noise (modified NASA 30P30N Configuration) 8. Acoustic Propagation Phase of Airframe Noise Prediction


new since BANC-II


Motivation behind BANC activity

Introduction

Workshop categories: 1. Airfoil trailing edge noise (TEN) 2. Unsteady wake interference between a pair of inline tandem cylinders 3. Minimal 4-wheel landing gear 4. Partially-dressed, cavity-closed nose landing gear 5. The LAGOON Simplified Landing Gear configuration tested by

Airbus and ONERA 6. Slat Noise (DLR/ONERA Configuration) 7. Slat Noise (modified NASA 30P30N Configuration) 8. Acoustic Propagation Phase of Airframe Noise Prediction


new since BANC-II


Motivation behind BANC activity

Introduction

AIAA-2013-2123 AIAA-2015-2847 BANC-II-1 BANC-III-1

BANC-III-1 problem statement Simulation matrix

Test cases Provide cp(x1), cf(x1), near-wake mean flow / turbulence profiles, surface

pressure spectra Gpp(f), FF noise Lp(fc) for CASES#1-5 (Re = 1–1.5 Mio.)

Case#1 56 m/s 0°

Case#2 55 m/s 4°

Case#3 53 m/s 6°

Case#4 38 m/s 0°

Case#5 60 m/s 4°

CASE#1: single core test case for those who could not afford the full matrix For the full problem statement with more specified definitions of

profile coordinates (sharp TE!) tripping devices (TBL-TE noise!) TBL transition locations ambient conditions, etc. data formatting instructions

including templates

please contact [email protected].

www.DLR.de • Chart 5 DU-96-180

NACA0012


BANC-III-1 participants:

1) PoliTo: Andrea Iob, wavePRO, Torino, & Renzo Arina, Politecnico di Torino, Italy & Paul Batten / S. Chakravarthy, Metacomp Technologies, USA (CA)

2) DLR: Roland Ewert / Christof Rautmann, German Aerospace Center

3) IAG: Dimitrios Bekiropoulos / Mohammad Kamruzzaman, University of Stuttgart, Germany

4) DTU: Franck Bertagnolio, DTU Wind Energy, Technical University of Denmark

BANC-III-1 contributions & participants Overview

Overview on contributions


BANC-III-1 contributions & participants Overview

configuration/ participant PoliTo DLR IAG DTU

Case#1 56 m/s 0°

Case#2 55 m/s 4° -

Case#3 53 m/s 6° -

Case#4 38 m/s 0° -

Case#5 60 m/s 4° -

Overview on contributions


Overview of methods Contribution PoliTo: IDDES with Large Eddy STimulation (CFD++ Code, Metacomp)

Hybrid RANS/LES coupled with Large-Eddy STimulation LEST automatically converts RANS statistics into fluctuating turbulent

velocity fields, suitable for sustaining an embedded LES

Stochastic velocity field

Mean Flow Near Field Far Field

FWH Time integration

Corrections 2D → 3D Span

Synthetic turbulence Fourier-based method Implemented in CFD++

IDDES Implemented in CFD++

RANS SA model QCR terms

cf. AIAA-2014-2764; Iob et al. www.DLR.de • Chart 8

Overview of methods

000 ,, pu ρCFD

RANS

4D-Stochastic Sound Sources FRPM*

CAA APE Sound Field

vortex sound sources

turbulence

source

p′

Spectral analysis

*Ewert, Comp. & Fluids (Vol. 37)

mean flow; here: DLR code TAU with SST

PIANO: Perturbation Investigation of Aeroacoustic Noise Contribution DLR: CAA-code PIANO with stochastic source model FRPM*

T

T

cf. AIAA-2014-3298; Rautmann et al. www.DLR.de • Chart 9

Simplified theoretical airfoil trailing-edge far-field noise prediction model based on steady RANS: highly accurate and very fast

Overview of methods Contribution IAG: simplified theoretical prediction code Rnoise of ‘Blake-TNO’-type

Rnoise: RANS based trailing-edge noise prediction model

Source Modeling RANS Simulation

Governing Eqns. (Brooks & Hodgson, 1981)

(Parchen, 1998)


Simplified theoretical airfoil trailing-edge far-field noise prediction model based on steady RANS: highly accurate and very fast

Overview of methods Contribution IAG: simplified theoretical prediction code Rnoise of ‘Blake-TNO’-type

Rnoise: RANS based trailing-edge noise prediction model

Source Modeling RANS Simulation

Governing Eqns.

Noise Spectra WPF BL &

Correlations

Wind Tunnel Exp. & Validation


Airfoil flow calculation – EllipSys2D Classical 2D incompressible RANS solver k−ω SST turbulence model

Surface pressure calculation – Blake-TNO Using anisotropic turb. stress tensor by stretching each direction of space using stretching factors Stretching factors tuned using mean pressure gradient (Bertagnolio et al., 2014)

Farfield noise – diffraction theory (Brooks & Hodgson, 1981) Important note concerning calculations:

Trip-tapes are modeled by fixing transition and using higher intermittency factor in Re−θ transition model

Overview of methods

TEN modeling @ DTU Wind Energy Contribution DTU: simplified theoretical prediction of ‘Blake-TNO’-type


IAG-LWT 2-point correlation measurements

u∞

α

x1/ lc

x 2/l c

0 0.2 0.4 0.6 0.8 1 1.2-0.3

-0.2

-0.1

0

0.1

0.2

0.3 midspan plane

x3

x1

x2

orientation of flow profilesposition @ 100.38 % lc

θ θ= 0°

Aerodynamical data

Near-wake flow characteristics

Overall comparisons


Near-wake flow characteristics CASE#1 SS Aerodynamical data

Overall comparisons


U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWT

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

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20

25

30

35CASE#1, IAG LWT

<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

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20

25

30

35CASE#1, IAG LWT

<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWT

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30

35CASE#1, IAG LWT

Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWT

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

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20

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35CASE#1, IAG LWT

Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWT

www.DLR.de • Chart 14 Λ, mmkT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWT


Overall comparisons


U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWT

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWT

<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWT

<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

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35CASE#1, IAG LWT

PoliTo: CFD++ + SA (+ QCR terms) DLR: TAU + SST (4 : 2 : 1) IAG: FLOWer + SST (+ anisotropy model) DTU: EllipSys2D + SST

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30

35CASE#1, IAG LWT

Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWT

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30

35CASE#1, IAG LWT

Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWT

www.DLR.de • Chart 15 Λ, mmkT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWT

U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliTo

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


PoliTo

kT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


Aerodynamical data

Overall comparisons


PoliTo

DLR


U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLR

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, DLR RSM

<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


kT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, DLR

Λ, mmΛ, mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, DLR

Near-wake flow characteristics CASE#1 SS

Aerodynamical data

Overall comparisons

PoliTo

DLR

IAG


U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, IAG

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, DLR RSMCASE#1, IAG

kT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, DLRCASE#1, IAG

Λ, mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, DLRCASE#1, IAG

www.DLR.de • Chart 17 Λ, mm


Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, DLRCASE#1, IAGCASE#1, DTU

Λ, mm

Aerodynamical data

Overall comparisons



PoliTo

DLR

IAG U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, IAGCASE#1, DTU

<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, DLR RSMCASE#1, IAGCASE#1, DTU

<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


DTU

kT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#1, IAG LWTCASE#1, PoliToCASE#1, DLRCASE#1, DLR RSMCASE#1, IAGCASE#1, DTU

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30




Overall comparisons



DLR

IAG

DTU

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30


U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30


Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30


<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#4, IAG LWTCASE#4, DLRCASE#4, DLR RSMCASE#4, IAG

<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30

35CASE#4, IAG LWTCASE#4, DLRCASE#4, DLR RSMCASE#4, IAGCASE#4, DTU

Λ, mmkT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30



Overall comparisons



DLR

IAG

DTU

U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30


ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30


Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30


<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


Λ, mmkT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30



Overall comparisons



DLR

IAG

DTU

U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30

35CASE#3, IAG LWTCASE#3, IAGCASE#3, DLRCASE#3, DTU

ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30


Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30


<u1u1>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u3u3>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


Λ, mmkT, m2/s2

x 2,m

m

0 10 20 30 400

5

10

15

20

25

30


Λf , mm

x 2,m

m

0 5 100

5

10

15

20

25

30

35CASE#5, DLRCASE#5, IAGCASE#5, DTU


Overall comparisons



DLR

IAG

DTU

U1, m/s

x 2,m

m

0 20 40 600

5

10

15

20

25

30


ε, m2/s3

x 2,m

m

100 101 102 103 1040

5

10

15

20

25

30


<u2u2>, m2/s2

x 2,m

m

0 20 400

5

10

15

20

25

30


<u1u1>, m2/s2

x 2,m

m

0 20 400

5

10

15

20

25

30

35CASE#5, DLRCASE#5, IAG

<u3u3>, m2/s2

x 2,m

m

0 20 400

5

10

15

20

25

30


Λ, mm

No measured comparison data available!

kT, m2/s2

x 2,m

m

0 20 400

5

10

15

20

25

30


x1/ lc

x 2/l c

0 0.2 0.4 0.6 0.8 1 1.2-0.3

-0.2

-0.1

0

0.1

0.2

0.3 midspan plane

x3

x1

x2

Surface pressure data

Overall comparisons

Position @ 99 % lc PSDs (measurement data normalized to ∆f = 1 Hz)

SS

PS


Surface pressure data

Unsteady surface pressure PSD Gpp(f) CASES#1 & #2

Overall comparisons


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#1-SS, IAG LWT

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#1-SS, IAG LWTCASE#1-PS, PoliToCASE#1-SS, PoliTo

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#1-SS, IAG LWTCASE#1-PS, PoliToCASE#1-SS, PoliToCASE#1-PS, DLRCASE#1-SS, DLR

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#1-SS, IAG LWTCASE#1-PS, PoliToCASE#1-SS, PoliToCASE#1-PS, DLRCASE#1-SS, DLRCASE#1-PS, IAGCASE#1-SS, IAG

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#1-SS, IAG LWTCASE#1-PS, PoliToCASE#1-SS, PoliToCASE#1-PS, DLRCASE#1-SS, DLRCASE#1-PS, IAGCASE#1-SS, IAGCASE#1-PS, DTUCASE#1-SS, DTU

PoliTo: IDDES-LEST DLR: PIANO-FRPM IAG: Rnoise ‘Blake-TNO’ derivative DTU: ‘Blake-TNO’ derivative

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#2-PS, IAG LWTCASE#2-SS, IAG LWTCASE#2-PS, DLRCASE#2-SS, DLR

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#2-PS, IAG LWTCASE#2-SS, IAG LWTCASE#2-PS, DLRCASE#2-SS, DLRCASE#2-PS, IAGCASE#2-SS, IAG

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#2-PS, IAG LWTCASE#2-SS, IAG LWTCASE#2-PS, DLRCASE#2-SS, DLRCASE#2-PS, IAGCASE#2-SS, IAGCASE#2-PS, DTUCASE#2-SS, DTU

Unsteady surface pressure PSD Gpp(f) CASES#3 & #5 Surface pressure data

Overall comparisons


no measured comparison data available!

PoliTo: IDDES-LEST DLR: PIANO-FRPM IAG: Rnoise ‘Blake-TNO’ derivative DTU: ‘Blake-TNO’ derivative

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#3-PS, IAG LWTCASE#3-SS, IAG LWTCASE#3-PS, DLRCASE#3-SS, DLR

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#3-PS, IAG LWTCASE#3-SS, IAG LWTCASE#3-PS, DLRCASE#3-SS, DLRCASE#3-PS, IAGCASE#3-SS, IAG

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#3-PS, IAG LWTCASE#3-SS, IAG LWTCASE#3-PS, DLRCASE#3-SS, DLRCASE#3-PS, IAGCASE#3-SS, IAGCASE#3-PS, DTUCASE#3-SS, DTU

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#5-PS, DLRCASE#5-SS, DLRCASE#5-PS, IAGCASE#5-SS, IAGCASE#5-PS, DTUCASE#5-SS, DTU

u∞

α

x1/ lc

x 2/l c

0 0.2 0.4 0.6 0.8 1 1.2-0.3

-0.2

-0.1

0

0.1

0.2

0.3 midspan plane b = 1 mr = 1m

x3

x1

x2

θ θ= 0°

θ = 90° orthogonalview direction fornoise prediction

Overall comparisons

b = 1 m r = 1 m 1/3-octave band spectra

θ = 90° chord-normal view direction for noise prediction


elliptic mirror @ DLR AWB

array @ UFAFF CPV @ IAG LWT

TEN farfield noise data

Farfield noise data

1/3-octave band FF noise spectra Lp(1/3)(fc) CASES#1 & #2

Overall comparisons


PoliTo: IDDES-LEST/FWH DLR: PIANO-FRPM IAG: Rnoise (‘Blake-TNO’ / Brooks & Hodgson) DTU: (‘Blake-TNO’ / Brooks & Hodgson)

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90 black/grey: measurement data

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#2, DLR

black/grey: measurement data

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, PoliTo


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, PoliToCASE#1, DLR


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, PoliToCASE#1, DLRCASE#1, IAG


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, PoliToCASE#1, DLRCASE#1, IAGCASE#1, DTU


1/3-octave band FF noise spectra Lp(1/3)(fc) CASES#3 & #5 Farfield noise data

Overall comparisons


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90 black: measurement data

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#5, DLR

black: measurement data

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#3, DLR


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80



Pressure data revisited to identify common trends;

are relative effects captured by the predictions?

Pressure data

Overall comparisons


Overall comparisons

Effect of flow velocity on Lp(1/3)(fc) and Gpp(f): CASE#1 vs. #4 Pressure data


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100black: measurement data

U∞ = 56 m/s U∞ = 38 m/s

U∞ = 56 m/s U∞ = 38 m/s

Overall comparisons



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90

CASE#1, DLRCASE#4, DLR

black/grey: measurement dataDLR simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, DLRCASE#1-SS, DLRCASE#4-PS, DLRCASE#4-SS, DLR

DLR simulationblack: measurement data

U∞ = 56 m/s U∞ = 38 m/s

U∞ = 56 m/s U∞ = 38 m/s

Overall comparisons



fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90


black/grey: measurement dataDLR simulation

fc, kHz

L p(1

/3),

dB5 10 152030

40

50

60

70

80

90

CASE#1, IAGCASE#4, IAG

black/grey: measurement dataIAG simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAGCASE#1-SS, IAGCASE#4-PS, IAGCASE#4-SS, IAG

IAG simulationblack: measurement data

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90


f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, DLRCASE#1-SS, DLRCASE#4-PS, DLRCASE#4-SS, DLR

DLR simulationblack: measurement data

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90

CASE#1, DTUCASE#4, DTU

black/grey: measurement dataDTU simulation

f, kHzG

pp,d

B(∆

f=1

Hz)

5 10 1550

60

70

80

90

100

CASE#1-PS, DTUCASE#1-SS, DTUCASE#4-PS, DTUCASE#4-SS, DTU

DTU simulationblack: measurement data

Overall comparisons

Effect of a-o-a on Lp(1/3)(fc): CASES#1 to #3 Pressure data


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90individual datasetsaveraged comparison spectraCASE#1CASE#2CASE#3

measurement data:

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, DLRCASE#2, DLRCASE#3, DLR

grey: measurement data

DLR simulation

fc, kHzL p

(1/3

),dB

5 10 152030

40

50

60

70

80

90CASE#1, DTUCASE#2, DTUCASE#3, DTU


DTU simulation

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90CASE#1, IAGCASE#2, IAGCASE#3, IAG


IAG simulation

Overall comparisons

Effect of a-o-a on Gpp(f): CASES#1 to #3 Pressure data

SS

PS

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#2-PS, IAG LWTCASE#3-PS, IAG LWT

measurement data

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-SS, IAGCASE#2-SS, IAGCASE#3-SS, IAG

IAG simulation

f, kHzG

pp,d

B(∆

f=1

Hz)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAGCASE#2-PS, IAGCASE#3-PS, IAG

IAG simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-SS, IAG LWTCASE#2-SS, IAG LWTCASE#3-SS, IAG LWT

measurement data

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, DLRCASE#2-PS, DLRCASE#3-PS, DLR

DLR simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)5 10 1550

60

70

80

90

100

CASE#1-SS, DLRCASE#2-SS, DLRCASE#3-SS, DLR

DLR simulation


Overall comparisons

Effect of a-o-a on Gpp(f): CASES#1 to #3 Pressure data

SS

PS

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAG LWTCASE#2-PS, IAG LWTCASE#3-PS, IAG LWT

measurement data

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-SS, IAGCASE#2-SS, IAGCASE#3-SS, IAG

IAG simulation

f, kHzG

pp,d

B(∆

f=1

Hz)

5 10 1550

60

70

80

90

100

CASE#1-PS, IAGCASE#2-PS, IAGCASE#3-PS, IAG

IAG simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-SS, IAG LWTCASE#2-SS, IAG LWTCASE#3-SS, IAG LWT

measurement data

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, DLRCASE#2-PS, DLRCASE#3-PS, DLR

DLR simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)5 10 1550

60

70

80

90

100

CASE#1-SS, DLRCASE#2-SS, DLRCASE#3-SS, DLR

DLR simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)5 10 1550

60

70

80

90

100

CASE#1-SS, DTUCASE#2-SS, DTUCASE#3-SS, DTU

DTU simulation

f, kHz

Gpp

,dB

(∆f=

1H

z)

5 10 1550

60

70

80

90

100

CASE#1-PS, DTUCASE#2-PS, DTUCASE#3-PS, DTU

DTU simulation


Overall comparisons

Effect of airfoil geometry on Lp(1/3)(fc): CASE#2 vs. CASE#5 Pressure data


fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90individual datasetsaveraged comparison spectraCASE#2CASE#5

measurement data:

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90



DLR simulation

fc, kHz

L p(1

/3),

dB

5 10 152030

40

50

60

70

80

90

CASE#2, IAGCASE#5, IAG


IAG simulation

fc, kHzL p

(1/3

),dB

5 10 152030

40

50

60

70

80

90

CASE#2, DTUCASE#5, DTU


DTU simulation

Overall comparisons

TEN directivities CASE#1 Pressure data


Overall comparisons

TEN directivities CASES#1 to #5 Pressure data


Conclusions

The outcome of the BANC-III workshop category 1 has been summarized. Results display a high scientific quality level; FF TEN predictions are within or

very close to the provided data scatter band, TEN maxima are principally well-predicted; but:

− General trends (a-o-a, velocity scaling) are not always correctly predicted. − Code-specific advantages/disadvantages are observable, indicating that a

methodology which comprehensively predicts all of the requested nearfield & FF quantities is not available to date.

The category 1 workshop problem remains a challenging simulation task due to

its high requirements on resolving/modeling of TBL source quantities.

We still faced a comparatively low number of participants, these were mainly developers of faster approaches dedicated for use in an industrial context (design-to-noise), BANC-III-1 results will hopefully activate multiplied follow-on activity by anyone interested to join the community.


Outlook

We hope to motivate a more representative spectrum of the TEN community to participate at BANC-IV-1 in 2016.

BANC-IV-1 will supplement the existing CASES#1–5 by additional datasets:

- 0.6-m chord NACA64-618 data provided by DTU Wind Energy; cp(x1), flow profiles, Lp(1/3)(fc), Gpp(f), spanwise correlation of Gpp; Re = 1.43 Mio.


A. Fischer, 2011 Case#6 45.03 m/s

-0.88°

Case#7 44.98 m/s 4.62°

array @ VTST

Outlook

We hope to motivate a more representative spectrum of the TEN community to participate at BANC-IV-1 in 2016.

BANC-IV-1 will supplement the existing CASES#1–5 by additional datasets:

- 0.6-m chord NACA64-618 data provided by DTU Wind Energy; cp(x1), flow profiles, Lp(1/3)(fc), Gpp(f), spanwise correlation of Gpp; Re = 1.43 Mio.

The by now established BANC category 1 data base is open for use to anyone

interested and will be maintained according to your feedback.

The BANC-IV-1 updated problem statement will be soon available; if you wish to be included in the distribution list please contact:

[email protected]


Thank you for your attention!



broadband trailing-edge noise · lest automatically converts rans statistics into ﬂuctuating...

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